Efficient mining of association rules using closed itemset lattices
Information Systems
Artificial chemistries—a review
Artificial Life
Evolving noisy oscillatory dynamics in genetic regulatory networks
EuroGP'06 Proceedings of the 9th European conference on Genetic Programming
Analog Genetic Encoding for the Evolution of Circuits and Networks
IEEE Transactions on Evolutionary Computation
Semi-supervised learning on closed set lattices
Intelligent Data Analysis
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In this paper, we propose an original computational approach to assist knowledge discovery in complex biological networks. First, we present an integrated model of the evolution of regulation networks that can be used to uncover organization principles of such networks. Then, we propose to use the results of our model as a benchmark for knowledge discovery algorithms. We describe a first experiment of such benchmarking by using gene knock-out data generated from the modeled organisms.